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Machine learning cognizant

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All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law.

Please read this Candidate Privacy Notice carefully as it informs you of how your personal information is processed in connection with your application for a job. By clicking the "Proceed" button, you acknowledge that you have read and understood this Notice. Machine Learning Engineer Romania, Romania. For this position you should be able to check the following: Knowledge of both traditional ML and Deep Learning techniques; Knowledge of advanced statistical techniques and concepts regression, properties of distributions, statistical tests, etc.

A day in the life of a Machine Learning Engineer: Assess the effectiveness of new data sources and data-gathering techniques; Real-world data is messy and dirty so prepare yourself to clean it, working with labeling teams or developing methods that programmatically label data; Go through research papers for possible solutions and be creative in developing custom models and algorithms to apply to datasets; Develop testing tools to monitor and analyze model performance and data accuracy.

With corporate headquarters in the United States and offices across more than 40 countries worldwide, Cognizant's global presence extends its delivery capability and amplifies its impact. As a premier AWS Partner, Cognizant can advance your digital transformation journey by modernizing your core to promote innovation.

Cognizant optimizes operations, drives efficiencies, unlocks new business opportunities. Explore AWS Partner highlights . Review AWS Competency details . Buy in AWS Marketplace .

Engage with AWS Partners for secure, innovative, and cost-effective custom solutions that leverage the power and scalability of AWS services to meet your needs.

Get Started with Cognizant. The Cognizant Machine Learning Model Lifecycle Orchestrator solution provides streamlined and enforced architecture best practices for machine learning ML model productionization. Benefits Reduce Time to Deploy Models. Reduced deployment of ML model from DS team development environment to production. Standardized end to end process, including automation for ML model build and deployment. Model Monitoring. Lower Cost.

How it works. Cognizant Team support to customize build and deployment of custom blueprints for complex model production scenario's. Register Docker images for custom algorithms that can be used for model deployment on an Amazon SageMaker endpoint.

Automatically deploys a trained model and provides an inference endpoint. View Deployment Status across all production models, along with capability to redeploy models with recent version from Model Registry. Monitors deployed machine learning models and detects any deviation in data quality and model quality.

Insight driven dashboards for Model Monitoring. One-touch deployment, Auto-scaling for pods, services, clusters, and right-sizing. Validated models deployed to target environment to serve predictions. Current State Analysis. Enterprise-Wide Accepted Tool Chain. Provide details of enterprise-wide accepted tools.

Business Motivator Goal. Identify the business motivator to move towards MLOps.